Abstract
A massive-training artificial neural network (MTANN) has been investigated for reduction of false positives (FPs) in computer-aided detection (CADe) of polyps in CT colonography (CTC). A major limitation of the MTANN is a long training time. To address this issue, we investigated the feasibility of a support vector regression (SVR) in the massive-training framework and developed a massive-training SVR (MTSVR). To test the proposed MTSVR, we compared it with the original MTANN in FP reduction in CADe of polyps in CTC. With MTSVR, we reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).
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Summers, R.M., Handwerker, L.R., Pickhardt, P.J., Van Uitert, R.L., Deshpande, K.K., Yeshwant, S., Yao, J., Franaszek, M.: Performance of a previously validated CT colonography computer-aided detection system in a new patient population. AJR. Am. J. Roentgenol 191, 168–174 (2008)
Suzuki, K., Yoshida, H., Nappi, J., Dachman, A.H.: Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes. Med. Phys. 33, 3814–3824 (2006)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1998)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Yoshida, H., Nappi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans. Med. Imaging 20, 1261–1274 (2001)
Metz, C.E.: ROC methodology in radiologic imaging. Investigative Radiology 21, 720–733 (1986)
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Xu, JW., Suzuki, K. (2011). False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography: A Massive-Training Support Vector Regression Approach. In: Yoshida, H., Cai, W. (eds) Virtual Colonoscopy and Abdominal Imaging. Computational Challenges and Clinical Opportunities. ABD-MICCAI 2010. Lecture Notes in Computer Science, vol 6668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25719-3_7
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DOI: https://doi.org/10.1007/978-3-642-25719-3_7
Publisher Name: Springer, Berlin, Heidelberg
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